We chose 5 FL baselines from two categories of FL methods. The first category includes FedAvg [1]}, FedProx [2]} and FedDF [3]}, in which clients share their full networks with the server. The second one includes FedSplit and FedGen [4]}, in which clients share only their public classifiers. More specifically, FedAvg is the most widely used FL method. FedProx introduces a proximal term in the local objective to regularize the local model training. FedDF utilizes knowledge distillation to aggregate local models on the server leveraging unlabeled public data. FedSplit shares only the public classifier of the local classification network to protect privacy. FedGen employs knowledge distillation to train a global generator, which in turn helps clients train local models. In this work, we implement FedGen based on FedSplit, in which only the public classifiers of local networks are shared. We also consider the local classification network trained solely based on local data as a baseline and denote it as Local.
